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1. Partitioned Search with Column Resampling for Locating Array Construction

   https://doi.org/10.1109/ICSTW.2019.00056  

   Lanus, Erin; Colbourn, Charles J.; Montgomery, Douglas C. (April 2019, IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW))

   Locating arrays are designs used in combinatorial testing with the property that every set of d t-way interactions appears in a unique set of tests. Using a locating array to conduct fault testing ensures that faulty interactions can be located when there are d or fewer faults. Locating arrays are fairly new and few techniques have been explored for their construction. Most of the available work is limited to finding only one fault. Known general methods require a covering array of strength t+d and produce many more tests than are needed. We present Partitioned Search with Column Resampling (PSCR), a randomized computational search algorithmic framework to verify if an array is (d t)-locating by partitioning the search space to decrease the number of comparisons. If a candidate array is not locating, random resampling is performed until a locating array is constructed or an iteration limit is reached. Results are compared against known locating array constructions from covering arrays of higher strength and against published results of mixed level locating arrays for parameters of real-world systems. The use of PSCR to build larger locating arrays from a variety of ingredient arrays is explored. 

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2. Types and Abstract Interpretation for Authorization Hook Advice

   Christian Skalka, David Darais (June 2020, Proceedings of the 2020 IEEE Computer Security Foundations Symposium (CSF))

   Authorization hooks are access control checks that prevent unauthorized principals from interacting with some protected resource, and are used extensively in critical software such as operating systems, middleware, and server programs. They are often intended to mediate information flow between subjects (e.g., file owners), but typically in an ad-hoc manner. In this paper we present a static type and effect system for detecting whether authorization hooks in programs properly defend against undesired information flow between subjects. A significant novelty of our approach is an integrated abstract interpretation-based tool that guides system clients through the information flow consequences of access control policy decisions. 

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3. Types and Abstract Interpretation for Authorization Hook Advice

   https://doi.org/10.1109/CSF49147.2020.00018  

   Skalka, C.; Darais, D.; Jaeger, T.; Capobianco, F. (June 2021, IEEE Computer Security Foundations Symposium)

   null (Ed.)

   Authorization hooks are access control checks that prevent unauthorized principals from interacting with some protected resource, and are used extensively in critical software such as operating systems, middleware, and server programs. They are often intended to mediate information flow between subjects (e.g., file owners), but typically in an ad-hoc manner. In this paper we present a static type and effect system for detecting whether authorization hooks in programs properly defend against undesired information flow between subjects. A significant novelty of our approach is an integrated abstract interpretation-based tool that guides system clients through the information flow consequences of access control policy decisions. 

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4. Improved Discrete Gaussian and Subgaussian Analysis for Lattice Cryptography

   https://doi.org/doi.org/10.1007/978-3-030-45374-9_21  

   Genise, N.; Micciancio, D.; Peikert, C.; Walter, M. (January 2020, Public-Key Cryptography – PKC 2020)

   Attribute-based encryption (ABE) is an advanced cryptographic tool and useful to build various types of access control systems. Toward the goal of making ABE more practical, we propose key-policy (KP) and ciphertext-policy (CP) ABE schemes, which first support unbounded sizes of attribute sets and policies with negation and multi-use of attributes, allow fast decryption, and are adaptively secure under a standard assumption, simultaneously. Our schemes are more expressive than previous schemes and efficient enough. To achieve the adaptive security along with the other properties, we refine the technique introduced by Kowalczyk and Wee (Eurocrypt’19) so that we can apply the technique more expressive ABE schemes. Furthermore, we also present a new proof technique that allows us to remove redundant elements used in their ABE schemes. We implement our schemes in 128-bit security level and present their benchmarks for an ordinary personal computer and smartphones. They show that all algorithms run in one second with the personal computer when they handle any policy or attribute set with one hundred attributes. [Note: this paper is not by the PI, but by Genise who was supported by the grant; support was acknowledged in this publication.] 

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5. Attributes in Multiple Facial Images

   https://doi.org/10.1109/FG.2018.00053  

   Liu, Xudong; Guo, Guodong (May 2018, IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018))

   Facial attribute recognition is conventionally computed from a single image. In practice, each subject may have multiple face images. Taking the eye size as an example, it should not change, but it may have different estimation in multiple images, which would make a negative impact on face recognition. Thus, how to compute these attributes corresponding to each subject rather than each single image is a profound work. To address this question, we deploy deep training for facial attributes prediction, and we explore the inconsistency issue among the attributes computed from each single image. Then, we develop two approaches to address the inconsistency issue. Experimental results show that the proposed methods can handle facial attribute estimation on either multiple still images or video frames, and can correct the incorrectly annotated labels. The experiments are conducted on two large public databases with annotations of facial attributes. 

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